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P2070-072 exam Dumps Source : IBM Content Collector Technical Mastery Test v1

Test Code : P2070-072
Test appellation : IBM Content Collector Technical Mastery Test v1
Vendor appellation : IBM
exam questions : 41 existent Questions

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instructions from IBM for Google, Amazon and fb | existent Questions and Pass4sure dumps

(MENAFN - The dialog) it's staggering when corporations ultimate for decades – or even more than a century – and especially so when they may be in a fast-altering business fondness desktop expertise. IBM, which traces its roots to the Eighties , grew from three petite organizations to a multi-billion-dollar suggestions know-how capabilities business today. Its united statesand downs along the manner tender some insights into the global expertise business, and might accommodate some instructive instructions for up-and-coming digital giants fondness Google, Amazon and fb – All of that are far more youthful than IBM.

In my current booklet, ' IBM: the upward thrust and descend and Reinvention of a worldwide Icon ,' I explore the enterprise's inheritance of developing and selling facts processing machine and software. As a former IBM employee and a historian , probably the most vital lesson I establish is that many people addle incremental adjustments in technology with greater fundamental ones that in fact shape the course of a company's fate.

there's a contrast between particular person items – successive models of PCs or typewriters – and the underlying applied sciences that manufacture them work. Over a hundred thirty years, IBM released neatly over three,600 hardware products and basically the identical amount of software . however All those gadgets and capabilities were in accordance with just a handful of actual technological advances, comparable to poignant from mechanical machines to people who relied on computing device chips and utility, and later to networks fondness the information superhighway. The transitions between these advances took location much more slowly than the constant stream of current products may indicate.

These transitions from the mechanical, to the digital, and now to the networked reflected an ever-turning out to be potential to assemble and utilize better quantities of suggestions without problems and quickly. IBM moved from manipulating statistical data to the utilize of applied sciences that educate themselves what people want and are interested in seeing.

a spotlight that may adapt

Between 1914 and 1918, IBM administration decided that the company the enterprise would be in was information processing. in additional concurrent phrases, that enterprise has whirl into 'massive statistics' and analytics. nonetheless it's nonetheless accumulating and organizing records, and performing calculations and computations on it.

on the grounds that the early Twenties, IBM has taken a disciplined routine to product progress and analysis, specializing in developing the underpinning applied sciences for its records processing products. Nothing gave the impress to be completed unintentionally.

A blue IBM punch card. Gwern/Wikimedia Commons

In its first half-century, IBM's primary technology platform from which many products emerged was the punch-card, yielding tabulators, card sorters, card readers and the notorious IBM Card . In its second half-century, the primary technology platform changed into the desktop, including mainframes, minicomputers, PCs and laptops. In its most concurrent 30 years, computing device earnings maintain introduced in a declining partake of the business's complete earnings, as IBM transitions to presenting more cyber web-primarily based functions, including software and technical and managerial consulting.

the upward thrust of each and every succeeding know-how happened throughout the maturity and decline of its predecessor. IBM first All started promoting computer systems within the Nineteen Fifties, however kept selling tabulating rig that nonetheless used punch playing cards unless the early Nineteen Sixties. As these days as the early Nineties, over ninety p.c of IBM's revenues came from selling computers, although it turned into introducing current features fondness management and technique consulting, assistance technology management and software income.

It wasn't except the conclusion of 2018 that IBM announced that fifty percent of its enterprise now got here from services and application, most of that maintain been current offerings developed within the archaic decade.

The tidings media – and even IBM employees – may maintain perceived that IBM became transforming itself quickly and frequently . really the business had planted seeds for growth early and punctiliously tended current technologies unless they bore fruit – fortunately, across the identical time as prior methods maintain been ending their positive lives.

This strategic routine isn't incredible – Apple has been selling own computer systems for more than forty years . Its administration, of direction, talks plenty more about its function in the smartphone enterprise, which is already beginning to stage off . Apple may additionally soon necessity – or already be engaged on – a brand current technological focus of attention to abide imperative.

The future of the giants

Microsoft, fondness Apple, evolved far from promoting just laptop software and operating techniques. It started web-based initiatives fondness its Bing search engine and OneDrive cloud storage – in addition to providing cloud-based mostly computing functions for groups.

IBM is already exploring quantum computing, as a brand current frontier of statistics processing. IBM research , CC via-ND

companies that began on the internet may additionally pan identical transitions. Amazon, Google and fb now and then declare to maintain converted themselves, but maintain not yet utterly left their customary agencies.

Amazon nevertheless makes most of its funds selling physical items online, although its internet-based mostly cloud services division is starting to be hastily . Amazon has moreover invested in a tall gain of other business that may develop in the future, comparable to fitness keeping and enjoyment content.

Google and fb quiet manufacture most of their money promoting counsel about how users behave to advertisers and corporations that are looking to appeal to americans to a selected factor of view. both are exploring other avenues, no matter if it be Google's self-driving automobiles or facebook's experiments with digital fact .

however at their core, All three web giants are quiet discovering current how you can capitalize on the large quantities of assistance they accumulate about shoppers' actions and interests – just as decades prior IBM discovered current easy methods to utilize tabulating rig and computers. if they're to final many years or centuries into the longer term, the corporations will should probe, test and innovate to ascertain current tips on how to income as technologies alternate.

IBM fb Google Innovation Amazon Alphabet MIT Press


Lessons from IBM for Google, Amazon and Facebook

BetterCloud Appoints Jim Brennan as Chief Product Officer | existent Questions and Pass4sure dumps

big apple, March 5, 2019 /PRNewswire/ -- BetterCloud, the main SaaS Operations administration platform, introduced nowadays it has multiplied its executive team with the appointment of Jim Brennan as Chief Product Officer. In his current position, Brennan will lead the company's product management, product operations and design groups. moreover, as a member of the govt management group, Brennan will be positive the building of the BetterCloud platform is aligned with the business's strategic path, principally its diverse go-to-market routine and focus of attention on bringing SaaS Operations administration to agencies international.

"Jim has worked at one of the crucial biggest and most valuable businesses in the world, including IBM, and the skills he brings in line with those experiences will be useful to BetterCloud as they continue to build on their business main SaaS Operations management platform," stated David Politis, CEO of BetterCloud. "Jim's deep technical potential in protection is exciting and his confirmed leadership abilities and entrepreneurial spirit are a flawless felicitous with their plans to hold BetterCloud's product construction efforts to the subsequent level."

Brennan brings greater than two decades of event within the building, administration, and marketing of expertise options, together with 18 years within the information security industry. Most currently, he served as vice chairman of Product administration at IBM where he turned into liable for All facets of product strategy, roadmap and execution across the business's safety Operations & Response portfolio. in this function, he led a global crew of product managers, and a yoke of go-practical groups involving engineering, marketing, earnings and support. He additionally held a number of Director-degree positions at the business. just before joining IBM, Brennan become Director of Product administration at Dell SecureWorks where he led a group of product managers across the machine administration, monitoring and log management services offerings.

"BetterCloud has built a pioneering acknowledge it truly is fundamentally altering the style agencies maneuver and secure applications within the cloud, and i could not extra excited to be section of the group right through this pivotal second of growth," mentioned Brennan. "every slight thing from their unbelievable crew of seasoned leaders and their world-category platform to the partnerships they've developed with businesses fondness Google, Okta and Dropbox, made it a straightforward option. The value BetterCloud is providing to businesses these days is unmatched and i look ahead to playing a key position during this subsequent chapter in BetterCloud's background."

About BetterCloudBetterCloud is the primary SaaS Operations administration platform, empowering IT and safety groups to define, remediate, and implement administration and safety guidelines for SaaS purposes. Over 2,500 customers in 60+ nations matter on BetterCloud for perpetual adventure monitoring, at once remediating threats, and entirely-automatic policy enforcement. To extra sheperd shoppers on their SaaS approach, the business established the first-ever "SaaS utility management and protection Framework" in its newly published ebook, Controlling Your SaaS environment. BetterCloud is headquartered in long island metropolis with an engineering office in Atlanta, GA. For extra assistance, please quest recommendation from

View long-established content material:

source BetterCloud

Copyright (C) 2019 PR Newswire. All rights reserved

Microsoft simply hired a primary variety officer — and IBM is suing over it | existent Questions and Pass4sure dumps

Tech agencies maintain a lower than stellar list hiring girls and minorities. but these companies will apparently consequence something it takes — including launching a prison battle — to rent one class of person: a first-rate diversity Officer.

IBM is suing its former revise gain officer Lindsay-Rae McIntyre after she received poached by Microsoft, in a case that might demonstrate just how essential variety, recruitment, and retention has become for tech companies.

McIntyre, who joined IBM in 2006, became named chief gain officer of Microsoft on Sunday, after serving within the identical position and as VP of human supplies at IBM. IBM, in its criticism, argues that McIntyree had access to diversity records, techniques, methodologies and initiatives that are confidential, and that she "will use, confidence on or disclose" these ideas in her current function.

On Monday, IBM became granted a brief restraining order in current york federal courtroom, which prevents McIntyre from working for Microsoft until the court docket decides otherwise.

"McIntyre become at the middle of tremendously exclusive and competitively sensitive assistance that has fueled IBM's success in these areas," a consultant for IBM mentioned in an announcement. "whereas they bear in humor Microsoft's necessity to hold keeping of mounting criticism of its record on variety, IBM intends to totally implement Ms. McIntyre's non-compete shrink to tender protection to their competitive counsel."

at the heart of IBM's criticism is an on-going lawsuit from 2015, Moussouris v. Microsoft employer. The lawsuit alleges that Microsoft systematically discriminated in opposition t women in technical and engineering roles at the business when it got here to opinions, pay, and promotions.

The briefings for that lawsuit wrapped up on February 9. Now it's up to the courts to manufacture a conclusion whether or now not the case has type-motion reputation.

As a section of its non-compete case, IBM is calling to manufacture utilize of Microsoft's personal criminal arguments from the Moussouris lawsuit to manufacture its own factor.

In April 2017, to steer clear of inevitable assistance from being launched publicly within the discovery process, Microsoft argued that such variety records is "no longer merely personal, but so elegant and doubtlessly destructive to Microsoft if published to its opponents (e.g., IBM), that the courtroom may quiet hold the surprising measure of inserting the tips under seal," in keeping with IBM's complaint.

Microsoft in the discontinue obtained some of the files in the case sealed, and others launched with redactions.

In its legitimate response to IBM, Microsoft argued that implementing McIntyre's non-compete shrink is "draconian," as McIntyre has promised no longer to partake private information with Microsoft.

it's worth noting that IBM's worker ranks aren't a nonpareil of variety: according to IBM's website, ladies comprised 31.8% of its international team of workers in 2016.

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Back to Microservices with Istio (Part 1) | existent questions and Pass4sure dumps

Istio is an Open Source project developed in partnership between teams from Google, IBM, and Lyft and it provides a solution to the complexities of microservice based application, to appellation a few:

  • Traffic management: Timeouts, retries, load balancing,
  • Security: End-user Authentication and Authorization,
  • Observability: Tracing, monitoring, and logging.
  • All of these can be solved in the application layer, but your services don’t discontinue up being so ‘Micro’ anymore, All the additional exertion for implementing these is a strain in the company’s resources, resources that can be used to deliver business value. Let’s hold an example:

    PM: How long will it hold to add a feedback feature?

    Dev: Two sprints.

    PM: What…? That’s just a CRUD!

    Dev: Creating the CRUD is the easy section but then they necessity to authenticate and accredit users and services. And because the network is not responsible they necessity to implement retries, and circuit breakers in the clients, and to manufacture positive that they consequence not hold the all system down they necessity Timeouts and Bulkheads, additionally to detect issues they necessity monitoring, tracing […]

    PM: Let’s just stick it in the Product Service then. Jeez!

    You rep the idea, All the ceremony, and exertion that must depart in, for us to add one service is enormous. In this article, we’ll showcase how Istio removes All the above-mentioned cross-cutting concerns from their services.

    Figure 1. The Ceremony of a Microservice

    Note: This article assumes that you maintain a working information of Kubernetes. If it’s not the case I recommend you to read my introduction to Kubernetes, and then continue with this article.

    The belief of Istio

    In a world without Istio one service makes direct requests to another and in cases of failure, the service needs to ply it by retrying, timeouting, opening the circuit breaker etc.

    Figure 2. Network traffic in Kubernetes

    To resolve this, Istio provides an ingenious solution by being completely separated from the services and act only by intercepting All network communication. And doing so it can implement:

  • Fault Tolerance — Using response status codes it understands when a request failed and retries.
  • Canary Rollouts — Forward only the specified percentage of requests to a current version of the service.
  • Monitoring and Metrics — The time it took for a service to respond.
  • Tracing and Observability — It adds special headers in every request and traces them in the cluster.
  • Security — Extracts the JWT Token and Authenticates and Authorizes users.
  • To appellation a few (for existent just a few) and rep you intrigued! Let’s rep to the Technical details!

    Istio’s Architecture

    Istio intercepts All network traffic and applies a set of rules by injecting an brilliant proxy as a sidecar in every pod. The proxies that enable All the features comprise The Data Plane and those are dynamically configurable by The Control Plane.

    The Data Plane

    The injected proxies enable Istio to easily achieve their requirements. For an sample let’s check out the retrying and Circuit breaking functionalities.

    Figure 3. How envoys implement Retries & CircuitBreaking

    To summarize:

  • Envoy sends the request to the first instance of service B and it fails.
  • The Envoy Sidecar retries. (1)
  • Returns a failed request to the calling proxy.
  • Which opens the Circuit Breaker and calls the next Service on subsequent requests. (2)
  • This means that you don’t maintain to utilize another Retry library, you don’t maintain to develop your own implementation of Circuit Breaking and Service Discovery in Programming Language X, Y or Z. All of those and more are provided out of the box by Istio and NO code changes are required.

    Great! Now you want to connect the voyage with Istio, but you quiet maintain some doubts, some open questions. Is this a One-Size-Fits-All-Solution, which you’re suspicious about, as it always ends up being One-Size-Fits nothing solution!

    You finally mutter the question: “Is this configurable?”

    Welcome to the cruise my friend and let’s rep introduced to the Control Plane.

    The Control Plane

    Is composed of three components: The Pilot, the Mixer, and the Citadel that in combination configure Envoys to route traffic, enforce policies and collect telemetry data. Visually presented in the image below.

    Figure 4. Control Plane in relation to Data Plane

    The envoys (i.e. the data plane) are configured using Kubernetes Custom Resource Definitions defined by Istio and specialized for this purpose. Which means that for you it’s just another Kubernetes Resource with a familiar syntax. Which after being created will be picked up by the control plane that applies it to the envoys.

    Relation of Services to Istio

    We described the relation of Istio to their Services, but not the other way around, what’s the relation of their Services to Istio?

    Frankly, their services maintain as much information of Istio’s presence, as fish consequence of water, they will request themselves “What the hell is water?”.

    Drawing by Victoria Dimitrakopoulos

    This means that you can pick a working cluster and after deploying the components of Istio, the services within will continue to work and in the identical manner, you can remove the components and everything will be just fine. Understandably, you would lose the capabilities provided by Istio.

    We had enough of theory, let’s achieve this information into practice!

    Istio in Practice

    Istio requires a Kubernetes Cluster with at least 4 vCPU and 8 GB of RAM. To quickly set up a cluster and ensue up with this article, I recommend using the Google Cloud Platform, which provides current users with a $300 free trial.

    After creating the cluster and configuring access with the Kubernetes command line instrument we’re ready to install Istio using the Helm Package manager.

    Installing Helm

    Install the Helm client on your computer as explained in the official docs. Which they will utilize to generate the Istio installation templates in the next section.

    Installing Istio

    Download Istio’s resources from the latest release, extract the contents into one directory that they will advert to as [istio-resources].

    To easily identify the Istio resources create a namespace istio-system in your Kubernetes Cluster:

    $ kubectl create namespace istio-system

    Complete the installation by navigating to [istio-resources] directory and executing the command below:

    $ helm template install/kubernetes/helm/istio \--set global.mtls.enabled=false \--set tracing.enabled=true \--set kiali.enabled=true \--set grafana.enabled=true \--namespace istio-system > istio.yaml

    The above command prints out the core components of Istio into the file istio.yaml. They customized the template using the following parameters:

  • global.mtls.enabled is set to deceptive to withhold the introduction focused.
  • tracing.enabled enables tracing of requests using jaeger.
  • kiali.enabled installs Kiali in their cluster for Visualizing Services and Traffic
  • grafana.enabled installs Grafana to visualize the collected metrics.
  • Apply the generated resources by executing the command:

    $ kubectl apply -f istio.yaml

    This marks the completion of the Istio installation in their cluster! Wait until All pods in the istio-system namespace are in Running or Completed status by executing the command below:

    $ kubectl rep pods -n istio-system

    Now we’re ready to continue with the next section, where they will rep the sample application up and running.

    Sentiment Analysis Application Architecture

    We will utilize the identical microservice application used in the Kubernetes Introduction article, it’s tangled enough to showcase Istio’s features in practice.

    The App is composed of four microservice:

  • SA-Frontend service: Serves the frontend Reactjs application.
  • SA-WebApp service: Handles requests for Sentiment Analysis.
  • SA-Logic service: Performs sentiment analysis.
  • SA-Feedback service: Receives feedbacks from the users about the accuracy of the analysis.
  • Figure 6 Sentiment Analysis microservices

    In motif 6, besides the services they discern the Ingress Controller which in Kubernetes routes incoming requests to the preempt services, Istio uses a similar concept called Ingress Gateway, which will be introduced in continuation of the article.

    Running the Application with Istio Proxies

    To ensue up with this article clone the repository istio-mastery, containing the application and the manifests for Kubernetes and Istio.

    Sidecar Injection

    The injection is done Automatically or Manually. To enable automatic sidecar injection, they necessity to label the namespace with istio-injection=enabled, by executing the command below:

    $ kubectl label namespace default istio-injection=enablednamespace/default labeled

    From now every pod that gets deployed into the default namespace will rep the sidecar injected. To verify this let’s deploy the sample application by navigating to the root folder of the [istio-mastery] repository and executing the following command:

    $ kubectl apply -f resource-manifests/kubepersistentvolumeclaim/sqlite-pvc createddeployment.extensions/sa-feedback createdservice/sa-feedback createddeployment.extensions/sa-frontend createdservice/sa-frontend createddeployment.extensions/sa-logic createdservice/sa-logic createddeployment.extensions/sa-web-app createdservice/sa-web-app created

    With the services deployed verify that the pods maintain two containers (the service and the sidecar) by executing the command kubectl rep pods and ensuring that under the column ready, they discern the value “2/2” indicating that both containers are running. As seen below:

    $ kubectl rep podsNAME READY STATUS RESTARTS AGEsa-feedback-55f5dc4d9c-c9wfv 2/2 Running 0 12msa-frontend-558f8986-hhkj9 2/2 Running 0 12msa-logic-568498cb4d-2sjwj 2/2 Running 0 12msa-logic-568498cb4d-p4f8c 2/2 Running 0 12msa-web-app-599cf47c7c-s7cvd 2/2 Running 0 12m

    Visually presented in motif 7.

    Figure 7. Envoy proxy in one of the Pods

    With the application up and running now they necessity to allow incoming traffic to gain their application.

    Ingress Gateway

    A best drill for allowing traffic into the cluster is through Istio’s Ingress Gateway which positions itself at the edge of the cluster and on incoming traffic enables Istio’s features fondness routing, load balancing, security, and monitoring.

    During Istio’s installation, the Ingress Gateway component and a service that exposes it externally were installed into the cluster. To rep the services External IP execute the command below:

    $ kubectl rep svc -n istio-system -l istio=ingressgatewayNAME kind CLUSTER-IP EXTERNAL-IPistio-ingressgateway LoadBalancer

    In the continuation of this article they will access the application on this IP (referred to as the EXTERNAL-IP), for convenience, rescue it in a variable by executing the command below:

    $ EXTERNAL_IP=$(kubectl rep svc -n istio-system \-l app=istio-ingressgateway \-o jsonpath='{.items[0].status.loadBalancer.ingress[0].ip}')

    If you gain this IP in your browser and you will rep a Service Unavailable error, as by default Istio blocks any incoming traffic until they define a Gateway.

    The Gateway Resource

    A Gateway is a Kubernetes Custom Resource Definition defined upon Istio’s installation in their cluster that enables us to specify the Ports, Protocol and Hosts for which they want to allow incoming traffic.

    In their scenario, they want to allow HTTP traffic on port 80, for All hosts. Achieved with the following definition:

    All the configuration is self-explanatory besides the selector istio: ingressgateway. Using this selector, they can specify to which Ingress Gateway to apply the configuration, and in their case, it’s the default ingress gateway controller installed on Istio setup.

    Apply the configuration by executing the command below:

    $ kubectl apply -f resource-manifests/istio/http-gateway.yaml created

    The gateway now allows access in port 80 but it has no concept where to route the requests. That is achieved using Virtual Services.

    The VirtualService resource

    The VirtualService instructs the Ingress Gateway how to route the requests that were allowed into the cluster.

    For their application requests coming through the http-gateway must be routed to the sa-frontend, sa-web-app and sa-feedback services (show in motif 8).

    Figure 8. Routes to be configured with VirtualServices

    Let’s wreck down the requests that should be routed to SA-Frontend:

  • Exact path /should be routed to SA-Frontend to rep the Index.html
  • Prefix path /static/* should be routed to SA-Frontend to rep any static files needed by the frontend, fondness Cascading Style Sheets and JavaScript files.
  • Paths matching the regex '^.*\.(ico|png|jpg)$' should be routed to SA-Frontend as it is an image, that the page needs to show.
  • This is achieved with the following configuration:

    The necessary points here are:

  • This VirtualService applies to requests coming through the http-gateway.
  • Destination defines the service where the requests are routed to.
  • Note: The configuration above is in the file sa-virtualservice-external.yaml, it moreover contains the configuration to route to SA-WebApp and SA-Feedback but was shortened for brevity.

    Apply the VirtualService by executing:

    $ kubectl apply -f resource-manifests/istio/ created

    Note: When they apply Istio resources the Kubernetes API Server creates an event received by Istio’s Control Plane which then applies the current configuration to the envoy proxies of every pod. And the Ingress Gateway controller is another Envoy which is configured by the Control Plane, visually presented in motif 9.

    Figure 9. Configuring Istio-IngressGateway to route requests

    The Sentiment Analysis app is now accessible on http://{EXTERNAL-IP}/. If you rep a Not establish status, consequence not worry sometimes it takes a bit for the configuration to depart in consequence and update the envoy caches.

    Before poignant to the next section utilize the app to generate some traffic.

    Kiali — Observability

    To access Kiali’s Admin UI execute the command below:

    $ kubectl port-forward \$(kubectl rep pod -n istio-system -l app=kiali \-o jsonpath='{.items[0]}') \-n istio-system 20001

    And open http://localhost:20001/ login using “admin” (without quotes) for user and password. There is a ton of useful features, for sample checking the configurations of Istio Components, visualizing services according to the information collected by intercepting network requests and answering, “who is calling who?”, “which version of a service has failures?” etc. hold some time to checkout Kiali before poignant on to the next goodie, visualizing metrics with Grafana!

    Figure 10. Kiali — Service Observability Grafana — Metrics Visualization

    The metrics collected by Istio are scraped into Prometheus and Visualized using Grafana. To access the Admin UI of Grafana execute the command below and open http://localhost:3000.

    $ kubectl -n istio-system port-forward \$(kubectl -n istio-system rep pod -l app=grafana \-o jsonpath={.items[0]}) 3000

    On the top left click the menu Home and select Istio Service Dashboard and on the top left corner select the service starting with sa-web-app, you will be presented with the collected metrics, as seen on the image below:

    Holly molly that’s an barren and totally non-exciting view, management would never certify of this. Let’s occasions some load by executing the command below:

    $ while true; consequence \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I value yogobella”}’; \sleep .8; done

    Now they maintain prettier graphs 😊, and additionally, they maintain the incredible tools of Prometheus for monitoring and Grafana for visualizing the metrics that enable us to know the performance, health, improvement or degradation of their services throughout time!

    Lastly, they will investigate Tracing requests throughout services.

    Jaeger — Tracing

    We necessity tracing because the more services they maintain the harder it gets to pinpoint the occasions of failure. Let’s hold the simple case in the image below:

    Figure 12. A commonly random failed request

    The request goes in, failure goes out, what was the cause? The first service? Or the second? Exceptions are in both, Let’s rep down to the logs of each. How many times consequence you find yourself doing this? Their job feels more fondness Software Detectives than Developers.

    This is a prevalent problem in Microservices and it’s solved using Distributed Tracing Systems where the services pass a unique header to each other and then this information is forwarded to the Distributed Tracing System where the request trace is achieve together. An sample is shown in motif 13.

    Figure 13. TraceId used to identify the span of a request

    Istio uses Jaeger Tracer that implements the OpenTracing API, a vendor-neutral framework. To rep access the Jaegers UI execute the command below:

    $ kubectl port-forward -n istio-system \$(kubectl rep pod -n istio-system -l app=jaeger \-o jsonpath='{.items[0]}') 16686

    Then open the UI in http://localhost:16686, select the sa-web-app service, if the service is not shown on the dropdown generate some activity on the page and hit refresh. Afterward click the button Find Traces, which displays the most recent traces, select any and a detailed breakdown of All the traces will be shown, as seen in motif 14.

    Figure 14. Jaeger — a request trace

    The trace shows:

  • The request comes to the istio-ingressgateway (it’s the first contact with one of the services so for the request the trace ID is generated) then the gateway forwards the request to the sa-web-app service.
  • In the sa-web-app service, the request is picked up by the Envoy sidecar and a span child is created (that’s why they discern it in the traces) and forwarded to the sa-web-app container instance.
  • Here the routine sentimentAnalysis handles the request. These traces are generated by the application, meaning that code changes were required).
  • From where a POST request is started to sa-logic. trace ID needs to be propagated by sa-web-app.
  • 5. …

    Note: At the 4th point their application needs to pick up the headers generated by Istio and pass them down on the next requests, as shown in the image below.

    Figure 15. (A) Istio propagating headers, (B) Services propagating headers

    Istio does the main ponderous lifting as it generates the headers on incoming requests, creates current spans on every sidecar, propagates them, but without their services propagating the headers as well, they will lose the full trace of the request.

    The headers to propagate are:


    Despite it being a simple task, there are already many libraries that simplify the process, for sample in the sa-web-app service, the RestTemplate client is instrumented to propagate the headers by simply adding the Jaeger and OpenTracing libraries in the dependencies.

    Note: The Sentiment Analysis app showcases implementations for Flask, Spring and ASP.NET Core.

    Now after investigating what they rep out of the box (or partially out of the box 😜) let’s rep to the main topic here, fine-grained routing, managing network traffic, security and more!

    Traffic Management

    Using the Envoy’s Istio provides a host of current capabilities to your cluster enabling:

  • Dynamic request routing: Canary deployments, A/B testing,
  • Load balancing: Simple and Consistent Hash balancing,
  • Failure Recovery: Timeouts, retries, circuit breakers,
  • Fault Injection: Delays, abort requests etc.
  • In the sequence of this article, we’ll showcase these capabilities in their application and rep introduced to current concepts along the way. The first concept they will delve into is DestinationRules and using those we’ll enable A/B Testing.

    A/B Testing — Destination Rules in Practice

    A/B Testing is used when they maintain two versions of an application (usually the versions vary visually) and they aren’t 100% positive which will enlarge user interaction, so they try both versions at the identical time and collect metrics.

    Execute the command below to deploy a second version of the frontend, needed for demonstrating A/B Testing:

    $ kubectl apply -f resource-manifests/kube/ab-testing/sa-frontend-green-deployment.yamldeployment.extensions/sa-frontend-green created

    The deployment manifest for the green version differs in two points:

  • The image is based on a different tag: istio-green and
  • Pods are labeled with version: green.
  • And as both deployments maintain the label app: sa-frontend requests routed by the virtual service sa-external-services to the service sa-frontend will be forwarded to All of its instances and will be load balanced using the round robin algorithm, which causes the issue presented in motif 16.

    Figure 16. Requested files are not found

    The files are not establish because they are named differently in the different versions of the app. Let’s verify that:

    $ curl --silent http://$EXTERNAL_IP/ | tr '"' '\n' | grep main/static/css/main.c7071b22.css/static/js/main.059f8e9c.js$ curl --silent http://$EXTERNAL_IP/ | tr '"' '\n' | grep main/static/css/main.f87cd8c9.css/static/js/main.f7659dbb.js

    This means that the index.html that requests one version of the static files might be load balanced to the pods delivering the other version, where understandably the other files consequence not exist.

    This means that for their app to work they necessity to insert the restriction that “the version of the app that served the index.html, must serve subsequent requests”.

    We’ll achieve this using Consistent Hash Loadbalancing, which is the process that forwards requests from the identical client to the identical backend instance, using a predefined property, fondness for sample an HTTP header. Made viable by DestionatioRules.


    After a request gets routed by the VirtualService to the revise service, then using DestinationRules they can specify policies that apply to the traffic intended for the instances of this Service, as seen in motif 17.

    Figure 17. Traffic Management with Istio Resources

    Note: motif 17, visualizes how Istio resources are affecting the network traffic, in an easily understandable way. But, to be precise the conclusion to which instance to forward the request to is made in the Ingress Gateway’s Envoy, configured by the CRDs.

    Using Destination Rules they can configure load balancing to maintain consistent hashing and ensure that the identical user is responded by the identical instance of the service. Achieved with the following configuration:

  • Generate a consistent hash according to the contents of the “version” header.
  • Apply the configuration by executing the command below and give it a try!

    $ kubectl apply -f resource-manifests/istio/ab-testing/ created

    Execute the command below and verify that you rep the identical files when specifying the version header:

    $ curl --silent -H "version: yogo" http://$EXTERNAL_IP/ | tr '"' '\n' | grep main

    Note: You can utilize this chrome extension to add different values to the version header, to test in your browser.

    DestinationRules maintain more LoadBalancing capabilities, for All the details check out the official docs.

    Before poignant on to explore VirtualService in more detail, remove the green version of the app and the destination rule by executing the commands below:

    $ kubectl delete -f resource-manifests/kube/ab-testing/sa-frontend-green-deployment.yamldeployment.extensions “sa-frontend-green” deleted$ kubectl delete -f resource-manifests/istio/ab-testing/ “sa-frontend” deleted Shadowing — Virtual Services in Practice

    Shadowing or Mirroring is used when they want to test a change in production but not strike end-users, so they mirror the requests into a second instance that has the change and evaluate it. To phrase it simpler it’s when one of your colleagues picks the most captious issue and makes a tall ball of mud tug Request, that nobody can really review.

    To test out this feature lets create a second instance of SA-Logic (which is buggy) by executing the command below:

    $ kubectl apply -f resource-manifests/kube/shadowing/sa-logic-service-buggy.yamldeployment.extensions/sa-logic-buggy created

    Execute the following command and verify that All instances are labeled with the respective versions and additionally with app=sa-logic:

    $ kubectl rep pods -l app=sa-logic --show-labelsNAME READY LABELSsa-logic-568498cb4d-2sjwj 2/2 app=sa-logic,version=v1sa-logic-568498cb4d-p4f8c 2/2 app=sa-logic,version=v1sa-logic-buggy-76dff55847-2fl66 2/2 app=sa-logic,version=v2sa-logic-buggy-76dff55847-kx8zz 2/2 app=sa-logic,version=v2

    As the service sa-logic targets pods labeled with app=sa-logic, any incoming requests will be load balanced between All instances, as shown in motif 18.

    Figure 18. Round Robin load balancing

    But they want requests to be routed to the instances with version v1 and mirrored to the instances with version v2, as shown in motif 19.

    Figure 19. Routing to v1 and Mirroring to v2

    This is achieved using a VirtualService in combination with a DestinationRule, where the destination rule specifies the subsets and VirtualService routes to the specific subset.

    Specifying Subsets with Destination Rules

    We define the subsets with the following configuration:

  • Host defines that this rule applies only when routing has occurred towards sa-logic service
  • Subset appellation used when routing to instances of a subset.
  • Label defines the key-value pairs that necessity to match for an instance to be section of the subset.
  • Apply the configuration executing the command below:

    $ kubectl apply -f resource-manifests/istio/shadowing/ created

    With the subsets defined they can travel on and configure a VirtualService to apply to requests towards sa-logic where the requests are:

  • Routed to the subset named v1 and,
  • Mirrored to the subset named v2.
  • And this is achieved with the manifest below:

    As everything is self-explanatory let’s just discern it in action:

    $ kubectl apply -f resource-manifests/istio/shadowing/ created

    Add some load by executing the following command:

    $ while true; consequence curl -v http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I value yogobella”}’; \sleep .8; done

    Check the results in Grafana, where they can discern that the buggy version is failing about 60% of the requests, but nothing of the failures affected the end-users as they were responded by the currently dynamic service.

    Figure 20. The success rate of sa-logic service versions

    In this section, they saw for the first time a VirtualService that was applied to the envoys of their services, when the sa-web-app makes a request towards sa-logic this goes through the sidecar envoy, which via the VirtualService is configured to route to the subset v1 and mirror to the subset v2 of the sa-logic service.

    I can discern you thinking “Darn man Virtual Services are simple!”, in the next section, we’ll extend the sentence to “Simply Amazing!”.

    Canary Deployments

    Canary Deployment is the process of rolling out a current version of an application to a petite set of users, as a step to verify the absence of issues, and then with a higher assurance of property release to the wider audience.

    We will continue with the identical buggy subset of sa-logic to demonstrate canary deployments.

    Let’s start boldly and forward 20% of the users to the buggy version (this represents the canary deployment) and 80% to the wholesome service by applying the VirtualService below:

  • Weight specifies the percentage of requests to be forwarded to the destination or subset of the destination.
  • Update the previous sa-logic virtual service configuration using the following commands:

    $ kubectl apply -f resource-manifests/istio/canary/ configured

    We immediately discern that some of their requests are failing:

    $ while true; consequence \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I value yogobella”}’ \--silent -w “Time: %{time_total}s \t Status: %{http_code}\n” \-o /dev/null; sleep .1; doneTime: 0.153075s Status: 200Time: 0.137581s Status: 200Time: 0.139345s Status: 200Time: 30.291806s Status: 500

    VirtualServices enable Canary Deployments and with this method, they reduced potential damages to 20% of their user base. Beautiful! Now they can utilize Shadowing and Canary Deployments every time they are insecure about their code, in other words always. 😜

    Timeouts & Retries

    It’s not always that the code is buggy. In the list of “The 8 fallacies of distributed computing” the first fallacy is that “The network is reliable”. The network is NOT reliable, and that’s why they necessity Timeouts and Retries.

    For demonstration purposes, they will continue to utilize the buggy version of sa-logic, where the random failures simulate the unreliability of the network.

    The buggy service has a one-third chance of taking too long to respond, one-third chance of ending in an Internal Server oversight and the repose complete successfully.

    To alleviate these issues and ameliorate the user undergo they can:

  • Timeout if the service takes longer than 8 seconds and
  • Retry on failed requests.
  • This is achieved with the following resource definition:

  • The request has a timeout of 8 seconds,
  • It attempts 3 times,
  • An attempt is marked as failed if it takes longer than 3 seconds.
  • This is an optimization, as the user won’t be waiting for longer than 8 seconds and they retry three times in case of failures, increasing the chance of resulting in a successful response.

    Apply the updated configuration with the command below:

    $ kubectl apply -f resource-manifests/istio/retries/ configured

    And check out the Grafana graphs for the improvement in success rate (shown in motif 21).

    Figure 21. Improvement after using Timeouts & Retries

    Before poignant into the next section delete sa-logic-buggy and the VirtualService by executing the command below:

    $ kubectl delete deployment sa-logic-buggydeployment.extensions “sa-logic-buggy” deleted$ kubectl delete virtualservice “sa-logic” deleted Circuit Breaker and Bulkhead patterns

    Two necessary patterns in Microservice Architectures that enable self-healing of the services.

    The Circuit Breaker is used to desist requests going to an instance of a service deemed as unhealthy and enable it to recover, and in the meantime client’s requests are forwarded to the wholesome instances of that service (increasing success rate).

    The Bulkhead pattern isolates failures from taking the all system down, to hold an example, Service B is in a vitiate status and another service (a client of Service B) makes requests to Service B this will result that the client will utilize up its own thread pool and won’t be able to serve other requests (even if those are not related to Service B).

    I will skip implementations of these patterns because you can check out implementations in the official docs and I’m way too excited to showcase Authentication and Authorization, which will be the subject of the next article.

    Part I — Summary

    In this article, they deployed Istio in a Kubernetes cluster and using its Custom Resource Definitions fondness Gateways, VirtualServices, DestinationRules and it’s components it enabled the following features:

  • Observability over their services by answering what services are running, how are they performing and how are they related, using Kiali.
  • Metric collection and visualization, with Prometheus and Grafana.
  • Request tracing with Jaeger (german for Hunter).
  • Full and fine-grained control over the network traffic, enabling Canary Deployments, A/B Testing, and Shadowing.
  • Easy implementation of Retries, Timeouts, and CircuitBreakers.
  • And All were viable without code changes or any additional dependencies, keeping your services small, easy to operate and maintain.

    For your progress team removing these cross-cutting concerns and centralizing them into Istio’s Control plane, means that current services are easy to be introduced, they aren’t resource-heavy as developers can focus in solving business problems. And up to now, no developer complained about “having to solve gripping business problems!”.

    I would value to hear your thoughts in the comments below and feel free to gain out to me on Twitter or on my page, and abide tuned for the next article where they tackle the final layers of Authentication and Authorization!

    Oracle Releases GraalVM 1.0, a Polyglot Virtual Machine and Platform | existent questions and Pass4sure dumps

    Oracle has announced the 1.0 release of GraalVM, a polyglot virtual machine and platform. The initial release includes the capability to Run Java and JVM languages (via bytecode) as well as full champion for JavaScript and Node.JS, with beta champion for Ruby, Python, R and LLVM bitcode.

    The overall platform comprises a number of components:

  • Graal - a JIT compiler written in Java
  • SubstrateVM - a lightweight wrapper to abstract away the execution container
  • Truffle - a toolkit and API for building language interpreters
  • The overall intent is to provide a polyglot language execution environment that can be embedded within another execution container - either an OpenJDK container or another possibility, such as within an Oracle or MySQL database.

    InfoQ spoke to Thomas Wuerthinger, research director at Oracle Labs, for an in-depth interview about GraalVM.

    InfoQ: Can you justify the history of GraalVM? Where did it approach from?

    Thomas Wuerthinger: The tall belief was that a compiler didn't maintain to maintain any built-in information of the language semantics. The understanding for this was that it was very clear from the beginning that groups within Oracle had interest in a very diverse gain of languages including JavaScript, R and Ruby.

    There were two key areas they felt they could ameliorate upon with some exploration:

  • The common belief of VM architects throughout the software industry at that time was that a language VM needed to be designed specifically with the semantics of the language in humor if you wanted to rep optimal performance.
  • The Java architects worked to champion multilingualism starting in Java 7 by conversions to Java bytecode by adding `invokedynamic` to the JVM specification. The GraalVM team felt that since Java bytecode had too much Java semantics built into it, a longer term exertion could be much more efficient and a 100% compatible implementation at that.
  • We moreover had the goal of demonstrating a fully-multilingual engine with competitive performance, and they started off some collaborations with universities to work on other languages.

    A group at Purdue did an implementation of R, and a group at UC Irvine did an implementation of Python in 2012-2014. A student intern worked on Ruby as a thesis project, and it had enough uncanny and gripping properties that they thought it was a profitable proof point that their multilingual compiler was fully general. Later they brought those language projects into Oracle Labs to rep an industrial-quality implementation for those languages.

    With the release of GraalVM 1.0, they feel that they maintain demonstrated both that a multilingual VM with competitive performance is possible, and that the best way to consequence it isn't through a language-specific bytecode fondness Java or Microsoft CLR.

    InfoQ: What are the key technologies that manufacture up GraalVM? How will they abet developers?

    Wuerthinger: The key technology belief in GraalVM is called "Partial Evaluation", which is a technique for converting interpreters into compilers automatically. This allows you to build current languages very quickly. Language developers can focus on just building an [Abstract Syntax Tree] AST interpreter, and consequence not maintain to worry about writing a code generator or other low-level runtime functionality fondness a garbage collector.

    A key technology they built into GraalVM is logical/physical data separation. This allows any reminiscence data layout to be made to appear fondness a local language object. Most VMs maintain a specific layout for objects (with expostulate headers often called "boxes"), and to maintain the semantics of an expostulate in the language, your data has to maintain the identical layout.

    This means that you maintain to allocate reminiscence and copy data to be an "object" in most VMs.

    In Graal, you can maintain zero-cost interop between languages, because a JavaScript expostulate can be made to behave fondness a JavaScript expostulate or an R expostulate or whatever, without the necessity for additional expostulate creation & data copies.

    This moreover allows, for example, exposing data from a custom binary format in the profile of high-level objects to a program without ever actually allocating those high-level objects.

    Another key technology is their LLVM interpreter. They maintain a GraalVM interpreter for LLVM bitcode, which is the output of the favorite LLVM compiler, which works well with languages targeting autochthonous binaries (like C++, FORTRAN, Rust, and so forth).

    One of the key problems in implementing languages is that they usually maintain libraries that are implemented in autochthonous code (usually C/C++), and they utilize an API that exposes some amount of the language internals. So, if you want to be compatible with a language ecosystem, you maintain to champion its autochthonous libraries.

    One of the problems with implementations fondness JRuby or Nashorn is that they don't work for modules containing autochthonous parts, which is a problem for compatibility.

    A final and divorce technology piece of GraalVM that was added later is called SubstrateVM. The belief here was to manufacture VMs embeddable on a mix and match basis. A conventional language VM fondness Java Hotspot wants to control everything: which threads are scheduled, how reminiscence allocations are parcelled out to those threads, access to the OS, etc.

    We wanted a technology layer that would allow an underlying system to provide some of those services, with SubstrateVM making up the missing pieces in terms of what is needed to champion a dynamic JIT compiler (like a garbage collector). When you are running in a database, for example, the database wants to control reminiscence management, thread allocations, and so forth.

    SubstrateVM is the "wafer-thin" VM that can sit on something fondness a database, so when you Run the Graal compiler there, the database can specify the maximum heap size rather than the Java mechanism, and the database can manage code artefacts rather than the JVM class loader, and so forth.

    One of the necessary ways the SubstrateVM operates is to consequence ahead-of-time compilation on Java code (like Graal) directly into a autochthonous binary, using a closed world assumption (all the classes to be used should be known at compile time). So, when you deploy the Graal compiler in a database, they hold Graal and some set of GraalVM languages and compile them into a autochthonous shared library, and they don't maintain to be compiled at runtime.

    InfoQ: What is Truffle? Why is it needed?

    Wuerthinger: Truffle is the API you maintain to write a GraalVM interpreter to, when you are building a current GraalVM language. The way interpreters work is that they first parse source code into what is called an Abstract Syntax Tree or AST. For example, if you wrote code like: c = a + b then the AST would accommodate two nodes, one for the add operator and having child nodes of a and b, and a second one for the assignment having child nodes of c and the add operator node.

    The interpreter internals fondness the AST necessity to be inherited from Truffle interfaces, and if you consequence so, the Graal compiler will watch the activity of the language interpreter to learn the semantics of the language, so that a compiler for that language can be generated.

    InfoQ: One of GraalVM's most visible components is the Graal compiler - a Java-based JIT compiler, just known as "Graal". Can you converse to some of the difficulties that are present in C2 (the existing C++ JIT in OpenJDK) that Graal is looking to travel past? Is it expected that Graal will supersede C2 / Tiered Compilation as default in the mainline OpenJDK?

    Wuerthinger: The Graal compiler is an experimental compiler in Java 10 and Oracle is optimistic that it can be the default in a future release.

    The tall odds that a compiler fondness Graal has is that it first compiles itself (since it is written in Java), and that makes it viable to write Graal in a much higher-level way than working directly on the AST in C++ as C2 does. For example, this makes it easier for Graal to consequence more aggressive inlining and expostulate eschew analysis, which allows more expostulate allocations to be removed from the program automatically. In general, the more abstractions your program has, the more the Graal compiler helps you, so you discern the biggest performance benefits in things fondness the Stream & Lambda champion in Java8 or in Scala.

    InfoQ: Oracle is heavily marketing GraalVM as a polyglot VM. How does the content of the 1.0 release vary to what was shipped as section of Java 10? Should, for example, JRuby developers pick one release over the other? Is GraalVM seen as a divorce project, or a section of OpenJDK? Will future releases of Graal be tied to the Java release cadence, or is it seen as independent?

    Wuerthinger: GraalVM and Java are 100% independent projects with large open source presence and with divorce release cadences, and they both grab pieces of technology from each other. Java 10 is grabbing the compiler from GraalVM (as an experimental option), and GraalVM inbounds the bulk of Java 8 as one of the supported languages. GraalVM inbounds lots of languages, not just Java, so they utilize technology from Node.js, JRuby, and from the GNU R implementation, among others.

    Like most technology producer/consumer relationships across organizations, the consumer waits for current technology from others to stabilize before including it. Similarly, The Java Platform Group has to be conservative about including current technology until they are positive it won't wreck existing deployments, and they maintain lots of backwards and forwards compatibility concerns, so there is going to be some lag.

    InfoQ: IBM's OpenJ9 runtime takes a rather different approach to GraalVM. Can you justify the main differences between OpenJ9 / OMR as you discern them, and converse to what makes GraalVM unique?

    Wuerthinger: OpenJ9 is trying to accelerate up existing sole language runtimes (like Ruby MRI) by adding some limited just-in-time compilation functionality to them. The odds of their approach is that it is easier to felicitous into an existing runtime while staying compatible. The major disadvantage however is that the benefits are much more limited.

    First, the performance capitalize of their Ruby prototype is very petite compared to the order of magnitude Ruby running on GraalVM achieves. The understanding is that GraalVM works with much more profiling and speculative optimizations while OMR keeps the data structures of the existing interpreter.

    Second, they require you to write a code generator and cannot automatically derive the compiled code from the interpreter via partial evaluation.

    Third, there is no built-in physical/logical data separation.

    This leads to most necessary fourth point: there is no competence with their approach for efficient language interoperability.

    OMR can give marginal improvement to existing single-language runtimes fondness Ruby MRI that are not well-optimized already. GraalVM uses a completely current way to write languages with an order of magnitude improvement and polyglot capabilities to Run any set of languages with zero overhead interoperability.

    InfoQ: What about wider community involvement in Graal? Twitter are on record as being early adopters and running Graal in production - who else is already using it? What has been your undergo of working with non-Oracle developers? What does the future progress of Graal gawk like? Will it adopt a similar model to OpenJDK as a whole?

    Wuerthinger: OpenJDK has a very different mission than GraalVM.

    OpenJDK has a large surface area for developers, since it is creating current language features and current libraries All the time. You maintain to change your code to deal with many of the changes to OpenJDK, and the mission is to rep community consensus on which changes are worthwhile. The OpenJDK community is very large and there are a lot of opinions, and their process helps rep to community consensus.

    GraalVM is about making code in languages and libraries that already exist Run more efficiently, in more places, and interoperate more, and not to change language semantics (for the most part). The only major piece of developer interface it has is the interoperabity API that allows you to invoke strange language functions in other runtimes, and they try to abide below the waterline as much as possible.

    The community for each language that GraalVM supports comes to its own conclusions on how it should evolve.

    As with OpenJDK, GraalVM has a lot of worthy contributions from vendors fondness Red Hat, Intel and AMD that are quite substantial. Red Hat took the initiative a while ago to build an ARM backend for GraalVM without much input from us, and that turned out to be very useful for us. You may be alert that Oracle invested in an ARM vendor recently, so now they are paying more attention to that ecosystem.

    Those vendors believe that GraalVM will rep enough uptake from others to warrant the investments, and their interest isn't primarily for their own usage. As you point out, Twitter has been contributing to GraalVM because they are using it themselves. There are some other major technology vendors using it that aren't going public the way Twitter has, and they maintain internal users at Oracle.

    InfoQ: Oracle is usually reticent to argue future roadmaps. Can you give us any indication of where the progress track of GraalVM might hold us in the next 18 months?

    Wuerthinger: Well, the GraalVM team is in Oracle Labs, so they maintain benevolent of the contradictory problem where they maintain lots of research projects that are public that may or may not depart anywhere beyond a paper in an academic conference. Oracle Labs has a cloud of graduate student collaborators looking to try out ideas using GraalVM.

    We maintain one student who was thinking about linking GraalVM into Chromium so that you could utilize any language in the browser and not just JavaScript, but I reflect it is very clear that it is unlikely ever to be released as an Oracle product. You can certainly gawk for academic papers based on GraalVM to discern some other crazy ideas. We've done investigations into some other languages as research projects that may or may not depart anywhere, based on community interest.

    What I can bid you about the roadmap is that they are working on supporting more platforms. The initial release was just for a Linux/X86 and Mac/X86 to minimise the "release stress". They are actively working on Windows champion and moreover on champion for the ARM platform (together with Red Hat). There is moreover quite some stabilisation and completeness work necessary for the languages that are currently marked as "experimental"  i.e., R, Ruby, and Python.

    In terms of integrations, there are a number of ways that they are going to integrate GraalVM more deeply into MySQL and the Oracle RDBMS that you will discern in the next 18 months. But how GraalVM will gawk fondness in 18 months will moreover very much depend on the overall community feedback and contributions.

    Highlights from Linux Kongress | existent questions and Pass4sure dumps

    Did you know...? is a subscriber-supported publication; they confidence on subscribers to withhold the entire operation going. please abet out by buying a subscription and keeping LWN on the net.

    September 27, 2006

    This article was contributed by Stacey Quandt

    The 13th annual International Linux System Technology Conference, moreover known as Linux Kongress, took Place September 5 - 8 in Nürnberg, Germany. As a technical Linux event Linux Kongress is smaller in scale than the Ottawa Linux Symposium and quiet the conference sessions and tutorials included a number of property talks from familiar members of the Linux and open source communities such as Heinz Mauelshagen, Lars Mueller, Theodore Ts'o, Volker Lendecke, Alan Robertson, and Daniel Phillips.

    A few of the talks stood out. One such talk was Felix von Leitner's presentation titled "Benchmarking, round 2: I/O Performance", in which he tested file system performance on Linux, Windows, OpenSolaris, NetBSD, FreeBSD, and OpenBSD in order to better understand the scalability of different operating systems and IP stack throughput. Based on von Leitner's benchmarking methodology Linux has the fastest file system - reiser4.

    The testing theme continued with Poornima Bangalore, whose presentation was on the topic of "Best Practices in Linux Kernel Testing." Her talk detailed many of the key differences between traditional and open source testing. She pointed out that mainline kernel testing is more challenging than testing many other open source projects because of the rapid progress and the different sub trees in the kernel: the stable kernels are released every 6 weeks or so, release candidate (-rc) kernels are available every week, and experimental (-mm) kernels are available every few days. Poornima shared best practices regarding kernel configuration, hardware configuration, test automation, test coverage, and first failure data capture.

    Heinz Mauelshagen gave a talk on device-mapper architecture features and the related target feature set. In the talk "Linux as a Hypervisor," Jeff Dike discussed the evolution of the hypervisor champion in the Linux kernel and how capabilities such as ptrace, AIO and O_DIRECT manufacture a contrast to virtual machines. He moreover talked about the implications of FUSE (filesystems in userspace) and the manageability benefits of exporting a UML filesystem to the host. Lars Marowsky-Bree's presentation on Heartbeat 2 and Xen explored Heartbeat's competence to manage Xen guests. He expanded on Heartbeat's architecture and its integration with Xen to enable resource reallocation, globally ordered recovery actions, and data focus automation policies using the Cluster Resource Manager (CRM).

    Mattias Rechenburg's presentation on "Using Enterprise Data Centers with OpenQRM" showcased the status of OpenQRM an open source project to achieve high-availability, scalability, and deployment, service and server virtualization on a variety of operation system. In spite of OpenQRM's pluggable architecture, the audience focused on the fact that it depends on a binary module which requires champion from Qlusters. The common sentiment from the audience was they were not interested if they couldn't rep champion from Red Hat, IBM, Hewlett-Packard etc.

    In "Real-Time Approaches to Linux," Ted Ts'o shared his perspective on enterprise real-time computing and how it differs from so-called traditional real-time computing. He emphasized the changing requirements in enterprise software and how towering throughput is not enough because customers increasingly moreover require latency guarantees, especially in particular military applications and trading systems. It was gripping to hear about the benefits and tradeoffs of different approaches to enterprise real-time including RTAI and Ingo Molnar's CONFIG_PREEMPT_RT.

    Ted suggested that guidelines outlined by his colleague Paul McKenney can be used to evaluate the different approaches to enterprise real-time. This includes property of service, the amount of code inspection required when a current feature is added, the API provided to applications, the relative complexity, failing isolation, and supported hardware and software configurations.

    Although IBM presently has only one customer that plans to deploy enterprise real-time computing, the competence to champion large SMP systems, TCP/IP, commercially available middleware, and databases makes it an area to watch in the future. Ted moreover elaborated on the features of IBM's real-time JVM/SDK (aka IBM Websphere Real-Time v1.0) such as RTSJ (Real-time specification for Java), the Metronome real-time garbage collector, and AOT (Ahead of Time Compilation). The talk emphasized that there are many current applications for real-time operating systems, and in particular enterprise real-time Linux.

    Maddog provided the final keynote on having fun with open source in his own inimitable way.

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